Dedicated vs Shared GPU Inference: Scaling AI Infrastructure

Lyceum publishes this article and competes in this market.

The Architecture of Isolation: Why Memory Bandwidth Matters

The primary bottleneck for Large Language Model (LLM) inference is memory bandwidth rather than compute cores. When you utilize shared GPU infrastructure, you are often competing for access to the High Bandwidth Memory (HBM3 or HBM3e) bus. Even with modern virtualization, multi-tenant environments can suffer from performance degradation when a neighboring container initiates a massive KV-cache update or a large model load.

Dedicated GPU inference eliminates this contention. By securing exclusive access to an NVIDIA H100 or B200, your application maintains the full HBM bandwidth of the hardware, which NVIDIA rates at 3 TB/s for the H100 SXM5. This isolation is critical for maintaining stable Time Per Output Token (TPOT) metrics. In production environments, a 10 percent variance in latency might seem negligible, but for interactive applications like real-time coding assistants or medical diagnostic tools, that variance can lead to a degraded user experience or timeout errors.

• Dedicated Inference

  Full access to HBM3e bandwidth, zero interference from other workloads, and predictable P99 latency.

• Shared Inference

  Multiplexed memory access, potential 'noisy neighbor' effects, and variable latency during peak regional demand.

Lyceum provides dedicated inference endpoints where the machine is exclusively yours. This ensures that your model performance remains deterministic, regardless of what other teams on the platform are doing. By utilizing NVIDIA software and vLLM, we provide an open-stack orchestration layer that maximizes the efficiency of these dedicated resources without the black-box limitations of proprietary engines.

The Economics of Scale: Finding the Crossover Point

The financial argument for shared inference is built on the premise of low utilization. If your model only processes a few hundred requests per day, paying for a dedicated H100 at a fixed hourly rate is inefficient. However, as your traffic grows, the per-token cost of shared APIs quickly surpasses the hourly cost of a dedicated instance. The 'crossover point' moves with sustained utilization: the steadier your traffic, the sooner a reserved instance becomes the cheaper option.

Consider a scenario where a team is serving a Llama 3.1 70B model. On a shared, per-token API, high-volume usage can result in monthly bills that far exceed the cost of a reserved instance. By moving to a dedicated VM, teams can realize significant cost savings compared to traditional hyperscalers. For example, while H100 instances carry high costs on major US-based clouds, Lyceum provides the same hardware in European data centers, with per-second billing and no egress fees.

To optimize these economics, we implement a scale-to-zero capability. This allows your dedicated machine to shut down during periods of inactivity, such as overnight or between batch processing runs. You only pay for the uptime required to serve your traffic, effectively bridging the gap between the flexibility of serverless and the performance of dedicated hardware.

Technical Challenges: Cold Starts and VRAM Management

One of the most significant technical hurdles in shared inference is the 'cold start' problem. When a request hits a shared environment that has scaled to zero, the system must pull the model weights from storage, load them into VRAM, and initialize the inference engine. For a 70B parameter model, this can take tens of seconds, which is unacceptable for real-time applications.

Dedicated infrastructure mitigates this through persistent VRAM residency. Because the GPU is yours, the model stays loaded and ready. Even when using scale-to-zero on Lyceum, our rapid VM provisioning and optimized container snapshots significantly reduce the time required to return to a 'warm' state. We utilize the AI-driven scheduling to predict VRAM requirements and runtime estimations, which helps in selecting the most efficient GPU for your specific model architecture, further reducing overhead.

Furthermore, dedicated instances allow for the use of custom CUDA kernels and specific quantization techniques (like FP8 or AWQ) that may not be supported in a restricted shared environment. This flexibility is vital for teams performing LLM fine-tuning or deploying specialized vision foundation models where every millisecond of optimization counts.

Compliance as a Technical Requirement: The EU Moat

For European startups and scale-ups, the choice of infrastructure is often dictated by legal necessity rather than performance alone. The EU AI Act and GDPR impose strict requirements on where data is processed and how it is protected. Many shared inference providers are based in the US and host data on US-owned servers, which can be a deal-breaker for teams in regulated industries like healthcare, pharma, or defense.

Using a US-based shared provider often means your data is subject to the Cloud Act, potentially violating EU data sovereignty. Lyceum is an EU-native inference platform that runs on European data centers in Spain, Paris and the Nordics, keeping your inference workloads in Europe. This is not only a compliance checkbox; it is a competitive advantage when selling to European enterprises that require provable data residency.

We treat GDPR-compliant processing in European data centers, zero data retention on inference, and a DPA with named sub-processors available on request as core parts of our technical stack. For a medical imaging company or a legal-tech startup, the ability to tell a customer that their data is processed on a dedicated GPU in a secure European data center that supports their GDPR compliance is a powerful differentiator that shared, multi-tenant US clouds cannot readily replicate.

Sources

[1] NVIDIA Hopper Architecture In-Depth; [2] vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention; [3] Mastering LLM Techniques: Inference Optimization